Antonio Šajatović


Evaluating Automatic Term Extraction Methods on Individual Documents
Antonio Šajatović | Maja Buljan | Jan Šnajder | Bojana Dalbelo Bašić
Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)

Automatic Term Extraction (ATE) extracts terminology from domain-specific corpora. ATE is used in many NLP tasks, including Computer Assisted Translation, where it is typically applied to individual documents rather than the entire corpus. While corpus-level ATE has been extensively evaluated, it is not obvious how the results transfer to document-level ATE. To fill this gap, we evaluate 16 state-of-the-art ATE methods on full-length documents from three different domains, on both corpus and document levels. Unlike existing studies, our evaluation is more realistic as we take into account all gold terms. We show that no single method is best in corpus-level ATE, but C-Value and KeyConceptRelatendess surpass others in document-level ATE.


TakeLab at SemEval-2017 Task 6: #RankingHumorIn4Pages
Marin Kukovačec | Juraj Malenica | Ivan Mršić | Antonio Šajatović | Domagoj Alagić | Jan Šnajder
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our system for humor ranking in tweets within the SemEval 2017 Task 6: #HashtagWars (6A and 6B). For both subtasks, we use an off-the-shelf gradient boosting model built on a rich set of features, handcrafted to provide the model with the external knowledge needed to better predict the humor in the text. The features capture various cultural references and specific humor patterns. Our system ranked 2nd (officially 7th) among 10 submissions on the Subtask A and 2nd among 9 submissions on the Subtask B.